Checklist for Artificial Intelligence in Medical Imaging (CLAIM): a guide for authors and reviewers
Open accessAbstract
well the data match the intended use of the model.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Current standards for reporting model performance in ML-based science often fall short in addressing issues caused by leakage.
“Current standards for reporting model performance in ML-based science often fall short in addressing issues caused by leakage.”
From Leakage and the reproducibility crisis in machine-learning-based science (2023) · cites this paper as [38]
Items 20–21 address a leakage-related reporting concern: authors should describe how data were partitioned and ensure that images of the same patient do not appear in multiple partitions. The paper does not say that current reporting standards often fall short on leakage, or establish that claim across ML-based science generally. Page numbers were not available in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measuredMost checklists currently in use are not developed for ML-based science in general but rather for specific scientific or research communities.
“Further, most checklists currently in use are not developed for ML-based science in general but rather for specific scientific or research communities.4,38”
From Leakage and the reproducibility crisis in machine-learning-based science (2023) · cites this paper as [38]
The Abstract identifies widely used checklists for particular kinds of research—diagnostic accuracy studies, observational studies, and trials—and presents CLAIM as a checklist for AI in medical imaging. It does not establish that most checklists currently in use are community-specific rather than designed for ML-based science generally. (LLM, conf=0.83)
Experimental — accuracy not yet measuredChecklists and model cards are one way to provide standard best practices for reporting details about ML models.
“Specifically, checklists and model cards are one way to provide standard best practices for reporting details about ML models.38,39,40”
From Leakage and the reproducibility crisis in machine-learning-based science (2023) · cites this paper as [38]
Study quality was assessed using QUADAS-2 and CLAIM tools.
“The quality of the selected studies was rigorously assessed using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) alongside the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2).23,24”
From Artificial Intelligence Detection and Segmentation Models: A Systematic Review and Meta-Analysis of Brain Tumors in Magnetic Resonance Imaging (2024) · cites this paper as [23]
Existing guidelines for evaluating AI and LLMs in healthcare do not cover RAG applications in clinical settings.
“However, these guidelines do not cover RAG applications in clinical settings, emphasizing the need for a more specific guideline.”
From Improving large language model applications in biomedicine with retrieval-augmented generation: a systematic review, meta-analysis, and clinical development guidelines (2025) · cites this paper as [12]
The Abstract (page unavailable) describes CLAIM as a guideline for AI research in medical imaging, including text analysis. Neither that passage nor the searched passages on RAG, large language models, and clinical applications assess whether existing guidelines cover clinical RAG applications; the absence of RAG from this paper does not establish the broader claim. (LLM, conf=0.74)
Experimental — accuracy not yet measured
Following guidelines for research documentation and complete reporting increases transparency.
“Following guidelines for research documentation and complete reporting increases transparency. 63,64”
From AI pitfalls and what not to do: mitigating bias in AI (2023) · cites this paper as [63]
Human-labeled annotations require a clear protocol and measurement of intra- and interannotator reliability to reduce recall, observer, or measurement biases.
“Human-labeled annotations (eg, segmentation masks or bounding boxes) require a clear protocol and measurement of intra- and interannotator reliability to reduce recall, observer, or measurement biases (24).”
From Mitigating Bias in Radiology Machine Learning: 1. Data Handling (2022) · cites this paper as [24]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials
the CONSORT Group, Schulz, K. F., Altman, D. G., & Moher, D. (2010) · BMC Medicine · doi:10.1186/1741-7015-8-18 · Cited by 13,371 · see citing works
CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials
Schulz, K. F., Altman, D. G., Moher, D., & for the CONSORT Group (2010) · BMJ · doi:10.1136/bmj.c332 · Cited by 9,329 · see citing works
Vandenbroucke, J. P., von Elm, E., Altman, D. G., Gøtzsche, P. C., Mulrow, C. D., Pocock, S. J., Poole, C., Schlesselman, J. J., Egger, M., & Initiative, F. T. S. (2007) · PLoS Medicine · doi:10.1371/journal.pmed.0040297 · Cited by 8,613 · see citing works
Radiomics: the bridge between medical imaging and personalized medicine
Lambin, P., Leijenaar, R. T. H., Deist, T. M., Peerlings, J., de Jong, E. E. C., van Timmeren, J., Sanduleanu, S., Larue, R. T. H. M., Even, A. J. G., Jochems, A., et al. (2017) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2017.141 · Cited by 5,688 · see citing works
Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative
Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L. M., Lijmer, J. G., Moher, D., Rennie, D., & de Vet, H. C. W. (2003) · BMJ · doi:10.1136/bmj.326.7379.41 · Cited by 3,215
Towards Complete and Accurate Reporting of Studies of Diagnostic Accuracy: The STARD Initiative
Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P., Irwig, L., Lijmer, J. G., Moher, D., Rennie, D., de Vet, H. C. W., et al. (2003) · Clinical Biochemistry · doi:10.1016/s0009-9120(02)00443-5 · Cited by 3,182 · see citing works
Improving the Quality of Reporting of Randomized Controlled Trials: The CONSORT Statement
Begg, C. (1996) · JAMA · doi:10.1001/jama.1996.03540080059030 · Cited by 3,127 · see citing works
A Critical Review of Recurrent Neural Networks for Sequence LearningPreprint
Lipton, Z. C., Berkowitz, J. A., & Elkan, C. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.00019 · Cited by 2,711 · see citing works
STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration
Cohen, J. F., Korevaar, D. A., Altman, D. G., Bruns, D. E., Gatsonis, C. A., Hooft, L., Irwig, L., Levine, D., Reitsma, J. B., de Vet, H. C. W., et al. (2016) · BMJ Open · doi:10.1136/bmjopen-2016-012799 · Cited by 2,570 · see citing works
STARD 2015: An Updated List of Essential Items for Reporting Diagnostic Accuracy Studies
Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C., Glasziou, P., Irwig, L., Lijmer, J. G., Moher, D., Rennie, D., de Vet, H. C. W., et al. (2015) · Radiology · doi:10.1148/radiol.2015151516 · Cited by 1,349 · see citing works
Preparing Medical Imaging Data for Machine Learning
Willemink, M. J., Koszek, W. A., Hardell, C., Wu, J., Fleischmann, D., Harvey, H., Folio, L. R., Summers, R. M., Rubin, D. L., & Lungren, M. P. (2020) · Radiology · doi:10.1148/radiol.2020192224 · Cited by 1,020 · see citing works
Luo, W., Phung, D., Tran, T., Gupta, S., Rana, S., Karmakar, C., Shilton, A., Yearwood, J., Dimitrova, N., Ho, T. B., et al. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5870 · Cited by 988 · see citing works
STARD 2015: An Updated List of Essential Items for Reporting Diagnostic Accuracy Studies
Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L., Lijmer, J. G., Moher, D., Rennie, D., de Vet, H. C. W., et al. (2015) · Clinical Chemistry · doi:10.1373/clinchem.2015.246280 · Cited by 984 · see citing works
Park, S. H., & Han, K. (2018) · Radiology · doi:10.1148/radiol.2017171920 · Cited by 894 · see citing works
Sample Size Estimation: How Many Individuals Should Be Studied?
Eng, J. (2003) · Radiology · doi:10.1148/radiol.2272012051 · Cited by 811 · see citing works
Handelman, G. S., Kok, H. K., Chandra, R. V., Razavi, A. H., Huang, S., Brooks, M., Lee, M. J., & Asadi, H. (2018) · American Journal of Roentgenology · doi:10.2214/ajr.18.20224 · Cited by 410 · see citing works
Geis, J. R., Brady, A. P., Wu, C. C., Spencer, J., Ranschaert, E., Jaremko, J. L., Langer, S. G., Borondy Kitts, A., Birch, J., Shields, W. F., et al. (2019) · Radiology · doi:10.1148/radiol.2019191586 · Cited by 388 · see citing works
Bluemke, D. A., Moy, L., Bredella, M. A., Ertl-Wagner, B. B., Fowler, K. J., Goh, V. J., Halpern, E. F., Hess, C. P., Schiebler, M. L., & Weiss, C. R. (2020) · Radiology · doi:10.1148/radiol.2019192515 · Cited by 343 · see citing works
Self-citationEQUATOR: reporting guidelines for health research
Altman, D. G., Simera, I., Hoey, J., Moher, D., & Schulz, K. (2008) · The Lancet · doi:10.1016/s0140-6736(08)60505-x · Cited by 325 · see citing works
EQUATOR: reporting guidelines for health research.
Altman, D. G., Simera, I., Hoey, J., Moher, D., & Schulz, K. (2008) · PubMed Central · Cited by 224 · see citing works
Bossuyt, P. M., & Reitsma, J. B. (2003) · The Lancet · doi:10.1016/s0140-6736(03)12122-8 · Cited by 146 · see citing works
Improving the value of clinical research through the use of Common Data Elements
Sheehan, J. R., Hirschfeld, S., Foster, E. D., Ghitza, U. E., Goetz, K., Karpinski, J., Lang, L., Moser, R. P., Odenkirchen, J. C., Reeves, D. M., et al. (2016) · Clinical Trials · doi:10.1177/1740774516653238 · Cited by 130 · see citing works
A Systematic Guide to Reviewing a Manuscript
Provenzale, J. M., & Stanley, R. J. (2005) · American Journal of Roentgenology · doi:10.2214/ajr.05.0782 · Cited by 122 · see citing works
Common Data Elements in Radiology
Rubin, D. L., & Kahn, C. E. (2016) · Radiology · doi:10.1148/radiol.2016161553 · Cited by 97 · see citing works
Self-citationGeis, J. R., Brady, A. P., Wu, C. C., Spencer, J., Ranschaert, E. R., Jaremko, J. L., Langer, S. G., Kitts, A. B., Birch, J. V., Shields, W. F., et al. (2019) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2019.07.028 · Cited by 97 · see citing works
Park, S. H., & Kressel, H. Y. (2018) · Journal of Korean Medical Science · doi:10.3346/jkms.2018.33.e152 · Cited by 66 · see citing works
A Standardised Approach for Preparing Imaging Data for Machine Learning Tasks in Radiology
Harvey, H., & Glocker, B. (2019) · Artificial Intelligence in Medical Imaging · doi:10.1007/978-3-319-94878-2_6 · Cited by 45 · see citing works
Bending the Artificial Intelligence Curve for Radiology: Informatics Tools From ACR and RSNA
Kohli, M. D., Alkasab, T. K., Wang, K., Heilbrun, M. E., Flanders, A. E., Dreyer, K. J., & Kahn, C. E. (2019) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2019.06.009 · Cited by 38 · see citing works
Self-citationSimera, I., & Altman, D. G. (2013) · International Journal of Clinical Practice · doi:10.1111/ijcp.12168 · Cited by 28 · see citing works
Artificial Intelligence, Real Radiology
Kahn, C. E. (2019) · Radiology Artificial Intelligence · doi:10.1148/ryai.2019184001 · Cited by 25 · see citing works
Self-citationEvidence-Based Radiology: A Primer in Reading Scientific Articles
Budovec, J. J., & Kahn, C. E. (2010) · American Journal of Roentgenology · doi:10.2214/ajr.10.4696 · Cited by 24 · see citing works
Self-citationLakhani, P. C., Kim, W. J., & Langlotz, C. P. (2012) · Radiology · doi:10.1148/radiol.12112438 · Cited by 21 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [31]31. International Committee of Medical Journal Editors. Clinical Trials. http://www.icmje.org/recommendations/browse/publishing-and-editorial- issues/clinical-trial-registration.html. Accessed February 20, 2020.
Cited by
Sorted by citations
Collins, G. S., Moons, K. G. M., Dhiman, P., Riley, R. D., Beam, A. L., Van Calster, B., Ghassemi, M., Liu, X., Reitsma, J. B., van Smeden, M., et al. (2024) · BMJ · doi:10.1136/bmj-2023-078378 · Cited by 4,718 · see citing works
Leakage and the reproducibility crisis in machine-learning-based science
Kapoor, S., & Narayanan, A. (2023) · Patterns · doi:10.1016/j.patter.2023.100804 · Cited by 1,387 · see citing works
“Further, most checklists currently in use are not developed for ML-based science in general but rather for specific scientific or research communities.(4,38) As a result, best practices for model reporting in ML-based science are underspecified.”
BackgroundRoberts, M., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., Aviles-Rivero, A. I., Etmann, C., McCague, C., Beer, L., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00307-0 · Cited by 1,044 · see citing works
Roberts, M. T., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., Avilés-Rivero, A. I., Etmann, C., McCague, C., Beer, L., et al. (2020) · Apollo (University of Cambridge) · doi:10.17863/cam.64000 · Cited by 902 · see citing works
AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications
Rashid, A. B., & Kausik, M. A. K. (2024) · Hybrid Advances · doi:10.1016/j.hybadv.2024.100277 · Cited by 764 · see citing works
AI applications to medical images: From machine learning to deep learning
Castiglioni, I., Rundo, L., Codari, M., Leo, G. D., Salvatore, C., Interlenghi, M., Gallivanone, F., Cozzi, A., D’Amico, N. C., & Sardanelli, F. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.02.006 · Cited by 737 · see citing works
Lekadir, K., Frangi, A. F., Porras, A. R., Glocker, B., Cintas, C., Langlotz, C. P., Weicken, E., Asselbergs, F. W., Prior, F., Collins, G. S., et al. (2025) · BMJ · doi:10.1136/bmj-2024-081554 · Cited by 957 · see citing works
“…data, evaluation criteria and results, biases and other limitations, and periodic audits and updates 49-51 ; (c) a publication based on existing AI reporting the bmj | BMJ 2025;388:e081554 | doi: 10.1136/bmj-2024-081554 standards 13 15 52 ; and (d) a risk management file (see traceability 1).”
InfluentialSelf-citationBias in artificial intelligence algorithms and recommendations for mitigation
Nazer, L. H., Zatarah, R., Waldrip, S., Ke, J. X. C., Moukheiber, M., Khanna, A. K., Hicklen, R. S., Moukheiber, L., Moukheiber, D., Ma, H., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000278 · Cited by 621 · see citing works
“A number of comprehensive frameworks and checklists have been developed, such as the Translational Evaluation of Healthcare AI (TEHAI) [46], the DECIDE-AI [47], the Consolidated Standards of Reporting Trials-Artificial Intelligence (CONSORT-AI) [48], the prediction model risk of bias assessment tool (PROBAST) [49], an…”
MethodChecklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update
Tejani, A. S., Klontzas, M. E., Gatti, A. A., Mongan, J. T., Moy, L., Park, S. H., Kahn, C. E., Panel, F. T. C. 2. U., Abbara, S., Afat, S., et al. (2024) · Radiology: Artificial Intelligence · doi:10.1148/ryai.240300 · Cited by 570 · see citing works
Self-citationde Hond, A. A. H., Leeuwenberg, A. M., Hooft, L., Kant, I. M. J., Nijman, S. W. J., van Os, H. J. A., Aardoom, J. J., Debray, T. P. A., Schuit, E., van Smeden, M., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-021-00549-7 · Cited by 499 · see citing works
Ethical and Bias Considerations in Artificial Intelligence/Machine Learning
Hanna, M. G., Pantanowitz, L., Jackson, B., Palmer, O., Visweswaran, S., Pantanowitz, J., Deebajah, M., & Rashidi, H. H. (2025) · Modern Pathology · doi:10.1016/j.modpat.2024.100686 · Cited by 535 · see citing works
The current and future state of AI interpretation of medical images
Rajpurkar, P., & Lungren, M. P. (2023) · New England Journal of Medicine · doi:10.1056/nejmra2301725 · Cited by 443 · see citing works
Kocak, B., Baessler, B., Bakas, S., Cuocolo, R., Fedorov, A., Maier-Hein, L., Mercaldo, N., Müller, H., Orlhac, F., Pinto dos Santos, D., et al. (2023) · Insights into Imaging · doi:10.1186/s13244-023-01415-8 · Cited by 471 · see citing works
“For example, CLAIM, being focused on artificial intelligence in medical imaging, lacks enough emphasis on the radiomic workflow and data sharing.”
MethodInfluentialAbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?
Ma, J., Zhang, Y., Gu, S., Zhu, C. G., Ge, C., Zhang, Y., An, X., Wang, C., Wang, Q., Liu, X., et al. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3100536 · Cited by 429 · see citing works
“reach high accuracy, but also can generalize well on data from different sources [8], [9].”
BackgroundExternal Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review
Yu, A. C., Mohajer, B., & Eng, J. (2022) · Radiology Artificial Intelligence · doi:10.1148/ryai.210064 · Cited by 455 · see citing works
Tam, T. Y. C., Sivarajkumar, S., Kapoor, S., Stolyar, A. V., Polanska, K., McCarthy, K. R., Osterhoudt, H., Wu, X., Visweswaran, S., Fu, S., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01258-7 · Cited by 424 · see citing works
Radiomics in Oncology: A Practical Guide
Shur, J. D., Doran, S., Kumar, S., Dafydd, D. A., Downey, K., O’Connor, J. P. B., Papanikolaou, N. K., Messiou, C., Koh, D., & Orton, M. R. (2021) · Radiographics · doi:10.1148/rg.2021210037 · Cited by 388 · see citing works
“To aid authors and to provide a framework for manuscript writing, there are various radiomic- and artificial intelligence–specific checklists, reporting guides, and radiomic quality scores that can be referred to (4,21,48,49), in addition to artificial intelligence extensions of familiar guidelines such as TRIPOD (Tra…”
BackgroundArtificial intelligence in dental research: Checklist for authors, reviewers, readers
Schwendicke, F., Singh, T., Lee, J., Gaudin, R., Chaurasia, A., Wiegand, T., Uribe, S. E., & Krois, J. (2021) · Journal of Dentistry · doi:10.1016/j.jdent.2021.103610 · Cited by 373 · see citing works
CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
Liu, J., Zhang, Y., Chen, J.-N., Xiao, J., Lu, Y., Landman, B. A., Yuan, Y., Yuille, A., Tang, Y., & Zhou, Z. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01934 · Cited by 347 · see citing works
“, performance on new data across many hospitals, rather than the performance tailored to a single dataset [39, 42].”
BackgroundDetecting caries lesions of different radiographic extension on bitewings using deep learning
Ros, A. G. C., Gehrung, S., Krois, J., Chaurasia, A., Rossi, J. G., Gaudin, R., Elhennawy, K., & Schwendicke, F. (2020) · Journal of Dentistry · doi:10.1016/j.jdent.2020.103425 · Cited by 325 · see citing works
Deep learning for caries detection: A systematic review
Mohammad‐Rahimi, H., Motamedian, S. R., Rohban, M. H., Krois, J., Uribe, S. E., Mahmoudinia, E., Rokhshad, R., Nadimi, M., & Schwendicke, F. (2022) · Journal of Dentistry · doi:10.1016/j.jdent.2022.104115 · Cited by 321 · see citing works
Koçak, B., D’Antonoli, T. A., Mercaldo, N. D., Alberich‐Bayarri, Á., Baeßler, B., Ambrosini, I., Андрейченко, А. Е., Bakas, S., Beets‐Tan, R. G. H., Bressem, K. K., et al. (2024) · Insights into Imaging · doi:10.1186/s13244-023-01572-w · Cited by 309 · see citing works
“MAIC-10 achieved a high correlation score to Check-list for Artificial Intelligence in Medical Imaging (CLAIM) [27], a widely used 42-item reporting checklist, despite being tested on a small number of publications from the journal in which it was published.”
BackgroundMethodSchachner, T., Keller, R., & v Wangenheim, F. (2020) · Journal of Medical Internet Research · doi:10.2196/20701 · Cited by 267 · see citing works
AI pitfalls and what not to do: mitigating bias in AI
Gichoya, J. W., Thomas, K., Celi, L. A., Safdar, N., Banerjee, I., Banja, J. D., Seyyed-Kalantari, L., Trivedi, H., & Purkayastha, S. (2023) · The British Journal of Radiology · doi:10.1259/bjr.20230023 · Cited by 277 · see citing works
“63,64 We encourage researchers to ensure that their work is reproducible, allowing for independent validation and bias detection.”
Reproducibility standards for machine learning in the life sciences
Heil, B. J., Hoffman, M. M., Markowetz, F. M., Lee, S., Greene, C. S., & Hicks, S. C. (2021) · Nature Methods · doi:10.1038/s41592-021-01256-7 · Cited by 261 · see citing works
Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE)
Kelly, B. S., Judge, C., Bollard, S. M., Clifford, S. M., Healy, G. M., Aziz, A., Mathur, P., Islam, S., Yeom, K. W., Lawlor, A., et al. (2022) · European Radiology · doi:10.1007/s00330-022-08784-6 · Cited by 252 · see citing works
“The quality of research design and reporting varies, and guidelines such as those issued by CONSORT [26] and the RSNA [27] should be followed to improve the quality of research overall.”
ESMO guidance for reporting oncology real-world evidence (GROW)
Castelo-Branco, L., Pellat, A., Martins-Branco, D., Valachis, A., Derksen, J. W. G., Suijkerbuijk, K. P. M., Dafni, U., Dellaporta, T., Vogel, A., Prelaj, A., et al. (2023) · Annals of Oncology · doi:10.1016/j.annonc.2023.10.001 · Cited by 247 · see citing works
Nam, D., Chapiro, J., Paradis, V., Seraphin, T. P., & Kather, J. N. (2022) · JHEP Reports · doi:10.1016/j.jhepr.2022.100443 · Cited by 232 · see citing works
“The Checklist for Artificial Intelligence in Medical Imaging (CLAIM) and the assessment checklist developed by the Fairness, Universality, Traceability, Usability, Robustness and Explainability AI (FUTURE-AI) initiative establish reporting guidelines for appraisal of AI studies.(102) Similarly, the checklists for esta…”
MethodAccelerating antibiotic discovery through artificial intelligence
Melo, M. C. R., Maasch, J. R. M. A., & de la Fuente-Nunez, C. (2021) · Communications Biology · doi:10.1038/s42003-021-02586-0 · Cited by 234 · see citing works
Artificial intelligence: A critical review of applications for lung nodule and lung cancer
de Margerie‐Mellon, C., & Chassagnon, G. (2022) · Diagnostic and Interventional Imaging · doi:10.1016/j.diii.2022.11.007 · Cited by 213 · see citing works
Shen, Y., Wu, N., Phang, J., Park, J., Liu, K., Tyagi, S., Heacock, L. C., Kim, S., Moy, L. P., Cho, K., et al. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101908 · Cited by 214 · see citing works
Self-citationCurrent applications and development of artificial intelligence for digital dental radiography
Putra, R. H., Doi, C., Yoda, N., Astuti, E. R., & Sasaki, K. (2021) · Dentomaxillofacial Radiology · doi:10.1259/dmfr.20210197 · Cited by 222 · see citing works
Ning, Y., Teixayavong, S., Shang, Y., Savulescu, J., Nagaraj, V., Miao, D., Mertens, M., Ting, D. S. W., Ong, J. C. L., Liu, M., et al. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00143-2 · Cited by 212 · see citing works
“As the TREGAI checklist focuses on ethical considerations of GenAI, research developing GenAI could use it in addition to existing model development guidelines (eg, TRIPOD 56,57 or CLAIM 58,59 ) and other tools to enhance ethical discussion (eg, the Ethical Operating System Toolkit 60 and the Organisation for Economic…”
MethodYoon, J. H., Strand, F., Baltzer, P., Conant, E. F., Gilbert, F. J., Lehman, C. D., Morris, E. A., Mullen, L. A., Nishikawa, R. M., Sharma, N., et al. (2023) · Radiology · doi:10.1148/radiol.222639 · Cited by 207 · see citing works
Self-citationDICE-Net: A Novel Convolution-Transformer Architecture for Alzheimer Detection in EEG Signals
Μιλτιάδους, Α., Gionanidis, E., Tzimourta, K. D., Γιαννακέας, Ν., & Tzallas, A. T. (2023) · IEEE Access · doi:10.1109/access.2023.3294618 · Cited by 193 · see citing works
“Radiology: Artificial Intelligence [62].”
InfluentialLiu, S., McCoy, A. B., & Wright, A. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf008 · Cited by 200 · see citing works
“…and LLMs in healthcare, including DECIDE-AI (Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence), 11 CLAIM (Checklist for Artificial Intelligence in Medical Imaging), 12 and CONSORT-AI (Consolidated Standards of Reporting Trials-AI).”
To buy or not to buy—evaluating commercial AI solutions in radiology (the ECLAIR guidelines)
Omoumi, P., Ducarouge, A., Tournier, A., Harvey, H., Kahn, C. E., Verchère, F. L.-D., dos Santos, D. P., Kober, T., & Richiardi, J. (2021) · European Radiology · doi:10.1007/s00330-020-07684-x · Cited by 171 · see citing works
Self-citationFeigerlova, E., Hani, H., & Hothersall-Davies, E. (2025) · BMC Medical Education · doi:10.1186/s12909-025-06719-5 · Cited by 166 · see citing works
“A checklist for artificial intelligence in medical imaging (CLAIM) was used for studies including AI in radiology [34] (Supplementary Table 4).”
Radiomics, machine learning, and artificial intelligence—what the neuroradiologist needs to know
Wagner, M. W., Namdar, K., Biswas, A., Monah, S. R., Khalvati, F., & Ertl-Wagner, B. B. (2021) · Neuroradiology · doi:10.1007/s00234-021-02813-9 · Cited by 165 · see citing works
Wei, L., Niraula, D., Gates, E. D. H., Fu, J. Y., Luo, Y., Nyflot, M. J., Bowen, S. R., Naqa, I. M. E., & Cui, S. (2023) · British Journal of Radiology · doi:10.1259/bjr.20230211 · Cited by 174 · see citing works
“Mongan et al proposed a CLAIM guide 70 to promote clear, transparent and reproducible scientific communication about AI’s application in medical images.”
Methods for clinical evaluation of artificial intelligence algorithms for medical diagnosis
Park, S. H., Han, K., Jang, H. Y., Park, J. E., Lee, J.-G., Kim, D. W., & Choi, J. (2023) · Radiology · doi:10.1148/radiol.220182 · Cited by 163 · see citing works
Checklist for Evaluation of Image-Based Artificial Intelligence Reports in Dermatology
Daneshjou, R., Barata, C., Betz‐Stablein, B., Celebi, M. E., Codella, N., Combalia, M., Guitera, P., Gutman, D. A., Halpern, A. C., Helba, B., et al. (2021) · JAMA Dermatology · doi:10.1001/jamadermatol.2021.4915 · Cited by 163 · see citing works
Reporting guidelines in medical artificial intelligence: a systematic review and meta-analysis
Kolbinger, F. R., Veldhuizen, G. P., Zhu, J., Truhn, D., & Kather, J. N. (2024) · Communications Medicine · doi:10.1038/s43856-024-00492-0 · Cited by 160 · see citing works
Deep learning-assisted ultra-fast/low-dose whole-body PET/CT imaging
Sanaat, A., Shiri, I., Arabi, H., Mainta, I., Nkoulou, R. N., & Zaidi, H. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-020-05167-1 · Cited by 159 · see citing works
“…for not using cross-validation since recent guidelines seem to suggest that although multiple internal cross-validation can be useful, independent validation using an external dataset for a single trained model is preferred over internal validation to properly evaluate generalizability [25, 26].”
MethodYang, N., Ser, J. D., Walsh, S. L. F., Schönlieb, C., Roberts, M. T., SELBY, I. A., Howard, K., Owen, J., Neville, J., Guiot, J., et al. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.01.001 · Cited by 152 · see citing works
Maleki, F., Ovens, K. L., Gupta, R., Reinhold, C., Spatz, A., & Forghani, R. (2022) · Radiology Artificial Intelligence · doi:10.1148/ryai.220028 · Cited by 162 · see citing works
“published the CLAIM checklist to aid authors and reviewers with best practices in artificial intelligence research in medical imaging (14).”
BackgroundMethodREFORMS: Consensus-based Recommendations for Machine-learning-based Science
Kapoor, S., Cantrell, E. M., Peng, K., Pham, T. H., Bail, C. A., Gundersen, O. E., Hofman, J. M., Hullman, J. R., Lones, M. A., Malik, M. M., et al. (2024) · Science Advances · doi:10.1126/sciadv.adk3452 · Cited by 191 · see citing works
“For example, the CLAIM checklist ( 35 ) provides best practices for reporting research on AI in medical imaging.”
InfluentialInterpretable machine learning for dementia: a systematic review
Martin, S. A., Townend, F. J., Barkhof, F., & Cole, J. H. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.12948 · Cited by 149 · see citing works
Drukker, K., Chen, W., Gichoya, J. W., Gruszauskas, N. P., Kalpathy-Cramer, J., Koyejo, S., Myers, K. J., Sá, R. C., Sahiner, B., Whitney, H. M., et al. (2023) · Journal of Medical Imaging · doi:10.1117/1.jmi.10.6.061104 · Cited by 149 · see citing works
Mitigating Bias in Radiology Machine Learning: 1. Data Handling
Rouzrokh, P., Khosravi, B., Faghani, S., Moassefi, M., Vera Garcia, D. V., Singh, Y., Zhang, K., Conte, G. M., & Erickson, B. J. (2022) · Radiology: Artificial Intelligence · doi:10.1148/ryai.210290 · Cited by 145 · see citing works
“…in Medical Imaging and items 1–5 of the Guiding Principles for Good Machine Learning Practice for Medical Device Development, recently introduced by the U.S. Food and Drug Administration, Health Canada, and the United Kingdom’s Medicines and Healthcare products Regulatory Agency (24,57).”
BackgroundCentral reading of ulcerative colitis clinical trial videos using neural networks
Gottlieb, K., Requa, J., Karnes, W., Chandra Gudivada, R., Shen, J., Rael, E., Arora, V., Dao, T., Ninh, A., & McGill, J. (2021) · Gastroenterology · doi:10.1053/j.gastro.2020.10.024 · Cited by 136 · see citing works
PRISMA AI reporting guidelines for systematic reviews and meta-analyses on AI in healthcare
Cacciamani, G. E., Chu, T. N., Sanford, D. I., Abreu, A., Duddalwar, V., Oberai, A., Kuo, C.-C. J., Liu, X., Denniston, A. K., Vasey, B., et al. (2023) · Nature Medicine · doi:10.1038/s41591-022-02139-w · Cited by 143 · see citing works
Self-citationIntroduction to radiomics for a clinical audience
McCague, C., Ramlee, S., Reinius, M., SELBY, I. A., Hulse, D., Piyatissa, P., Bura, V., Crispin‐Ortuzar, M., Sala, E., & Woitek, R. (2023) · Clinical Radiology · doi:10.1016/j.crad.2022.08.149 · Cited by 137 · see citing works
Momtazmanesh, S., Nowroozi, A. A., & Rezaei, N. (2022) · Rheumatology and Therapy · doi:10.1007/s40744-022-00475-4 · Cited by 138 · see citing works
“The Checklist for Artificial Intelligence in Medical Imaging (CLAIM) [22] and the guideline released by the National Health Service (NHS) for ‘‘good practice for digital and data-driven health technologies’’ [192] are examples of such recommendations.”
Artificial Intelligence for breast cancer detection: Technology, challenges, and prospects
Díaz, O., Rodríguez‐Ruiz, A., & Sechopoulos, I. (2024) · European Journal of Radiology · doi:10.1016/j.ejrad.2024.111457 · Cited by 152 · see citing works
A decade of radiomics research: are images really data or just patterns in the noise?
dos Santos, D. P., Dietzel, M., & Baeßler, B. (2020) · European Radiology · doi:10.1007/s00330-020-07108-w · Cited by 129 · see citing works
“Among the first steps, one possible approach could be to require authors and reviewers to follow checklists such as the aforementioned or the recently proposed Checklist for Artificial Intelligence in Medical Imaging [9].”
BackgroundRecent methodological advances in federated learning for healthcare
Zhang, F., Kreuter, D., Chen, Y., Dittmer, S., Tull, S., Shadbahr, T., Schut, M. C., Asselbergs, F. W., Kar, S., Sivapalaratnam, S., et al. (2024) · Patterns · doi:10.1016/j.patter.2024.101006 · Cited by 142 · see citing works
Hickman, S. E., Woitek, R., Le, E., Im, Y. R., Luxhøj, C. M., Avilés-Rivero, A. I., Baxter, G. C., Mackay, J., & Gilbert, F. J. (2021) · Radiology · doi:10.1148/radiol.2021210391 · Cited by 126 · see citing works
AI-Based Reconstruction for Fast MRI—A Systematic Review and Meta-Analysis
Chen, Y., Schönlieb, C., Lió, P., Leiner, T., Dragotti, P. L., Wang, G., Rueckert, D., Firmin, D. N., & Yang, G. (2022) · Proceedings of the IEEE · doi:10.1109/jproc.2022.3141367 · Cited by 126 · see citing works
Preetha, C. J., Meredig, H., Brugnara, G., Mahmutoglu, M. A., Foltyn‐Dumitru, M., Isensee, F., Kessler, T., Pflüger, I., Schell, M., Neuberger, U., et al. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00205-3 · Cited by 129 · see citing works
Environmental Sustainability and AI in Radiology: A Double-Edged Sword
Doo, F. X., Vosshenrich, J., Cook, T. S., Moy, L. P., Almeida, E. P. R. P., Woolen, S. A., Gichoya, J. W., Heye, T. J., & Hanneman, K. A. (2024) · Radiology · doi:10.1148/radiol.232030 · Cited by 124 · see citing works
Self-citationShamrat, F. M. J. M., Azam, S., Karim, A., Islam, M. R., Tasnim, Z., Ghosh, P., & Boer, F. D. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12050680 · Cited by 128 · see citing works
“Even patients might benefit from this technology [18,19].”
BackgroundMao, B., Zhang, L., Ning, P., Ding, F. H., Wu, F., Lu, G. G., Geng, Y., & Ma, J. (2020) · European Radiology · doi:10.1007/s00330-020-07056-5 · Cited by 120 · see citing works
Spadarella, G., Stanzione, A., D’Antonoli, T. A., Андрейченко, А. Е., Fanni, S. C., Ugga, L., Kotter, E., & Cuocolo, R. (2022) · European Radiology · doi:10.1007/s00330-022-09187-3 · Cited by 120 · see citing works
“Some editors and journals have already begun to move in this direction, and it is desirable for this trend to spread at least to the more visible publications in our field [68, 72, 73].”
BackgroundAdvancing health equity with artificial intelligence
Thomasian, N. M., Eickhoff, C., & Adashi, E. Y. (2021) · Journal of Public Health Policy · doi:10.1057/s41271-021-00319-5 · Cited by 117 · see citing works
“Regulators can look towards AI-reporting recommendations in academia as a reference when formulating validation criteria, as the research community has recently developed a number of checklists designed to quantify the robustness of artificial intelligence studies [24, 25].”
BackgroundCurrent challenges of implementing artificial intelligence in medical imaging
Saw, S. N., & Ng, K. H. (2022) · Physica Medica · doi:10.1016/j.ejmp.2022.06.003 · Cited by 117 · see citing works
“This checklist is supposed to be viewed as a “ best practice ” for authors and reviewers in future journal publications to improve the quality of AI papers in medical imaging [46].”
BackgroundMa, J., Zhang, Y., Gu, S., Ge, C., Mae, S., Young, A., Zhu, C., Yang, X., Meng, K., Huang, Z., et al. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00154-7 · Cited by 145 · see citing works
Prostate MRI radiomics: A systematic review and radiomic quality score assessment
Stanzione, A., Gambardella, M., Cuocolo, R., Ponsiglione, A., Romeo, V., & Imbriaco, M. (2020) · European Journal of Radiology · doi:10.1016/j.ejrad.2020.109095 · Cited by 115 · see citing works
Brain metastasis detection using machine learning: a systematic review and meta-analysis
Cho, S. J., Sunwoo, L., Baik, S. H., Bae, Y. J., Choi, B. S., & Kim, J. H. (2020) · Neuro-Oncology · doi:10.1093/neuonc/noaa232 · Cited by 115 · see citing works
Generalizability of deep learning models for dental image analysis
Krois, J., Ros, A. G. C., Chaurasia, A., Patil, R., Chaudhari, P. K., Gaudin, R., Gehrung, S., & Schwendicke, F. (2021) · Scientific Reports · doi:10.1038/s41598-021-85454-5 · Cited by 115 · see citing works
Sadr, S., Mohammad‐Rahimi, H., Motamedian, S. R., Zahedrozegar, S., Motie, P., Vinayahalingam, S., Omid Dianat, A., & Nosrat, A. (2022) · Journal of Endodontics · doi:10.1016/j.joen.2022.12.007 · Cited by 121 · see citing works
Jha, A. K., Bradshaw, T., Buvat, I., Hatt, M., Prabhat, K. C., Liu, C., Obuchowski, N., Saboury, B., Slomka, P. J., Sunderland, J. J., et al. (2022) · Journal of Nuclear Medicine · doi:10.2967/jnumed.121.263239 · Cited by 113 · see citing works
Equity in essence: a call for operationalising fairness in machine learning for healthcare
Wawira Gichoya, J., McCoy, L. G., Celi, L. A., & Ghassemi, M. (2021) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2020-100289 · Cited by 104 · see citing works
Hickman, S. E., Baxter, G. C., & Gilbert, F. J. (2021) · British Journal of Cancer · doi:10.1038/s41416-021-01333-w · Cited by 110 · see citing works
Deep learning for automated detection and numbering of permanent teeth on panoramic images
Estai, M., Tennant, M., Gebauer, D., Brostek, A., Vignarajan, J., Mehdizadeh, M., & Saha, S. (2021) · Dentomaxillofacial Radiology · doi:10.1259/dmfr.20210296 · Cited by 110 · see citing works
Hurkmans, C., Bibault, J.-E., Brock, K. K., van Elmpt, W., Feng, M., David Fuller, C., Jereczek-Fossa, B. A., Korreman, S., Landry, G., Madesta, F., et al. (2024) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2024.110345 · Cited by 110 · see citing works
Deep learning in pulmonary nodule detection and segmentation: a systematic review
Gao, C., Wu, L., Wu, W., Huang, Y., Wang, X., Sun, Z., Xu, M., & Gao, C. (2024) · European Radiology · doi:10.1007/s00330-024-10907-0 · Cited by 115 · see citing works
Javaheri, T., Homayounfar, M., Amoozgar, Z., Reiazi, R., Homayounieh, F., Abbas, E., Laali, A., Radmard, A. R., Gharib, M. H., Mousavi, S. A., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00399-3 · Cited by 125 · see citing works
Equity within AI systems: What can health leaders expect?
Gurevich, E., Hassan, B. E., & Morr, C. E. (2022) · Healthcare Management Forum · doi:10.1177/08404704221125368 · Cited by 108 · see citing works
Fraser, A. G., Biasin, E., Bijnens, B. H., Bruining, N., Caiani, E. G., Cobbaert, K., Davies, R. H., Gilbert, S., Hovestadt, L., Kamenjašević, E., et al. (2023) · Expert Review of Medical Devices · doi:10.1080/17434440.2023.2184685 · Cited by 103 · see citing works
“CLAIM Mongan 2020 Radiology: Artificial Intelligence [75] Radiology Artificial intelligence in medical imaging”
MethodBrady, A. P., Allen, B., Chong, J., Kotter, E., Kottler, N. E., Mongan, J., Oakden‐Rayner, L., dos Santos, D. P., Tang, A., Wald, C., et al. (2024) · Insights into Imaging · doi:10.1186/s13244-023-01541-3 · Cited by 102 · see citing works
“By monitoring for individual components of data drift, institutions could trigger re-evaluation of model performance depending on timing and severity of changes and initiate appropriate steps to safeguard patient care.”
BackgroundSelf-citationDeep learning in oral cancer- a systematic review
Warin, K., & Suebnukarn, S. (2024) · BMC Oral Health · doi:10.1186/s12903-024-03993-5 · Cited by 113 · see citing works
“…studies should address minimum standard guidelines, such as Standards for Reporting of Diagnostic Accuracy Study-AI (STARD-AI); standards for diagnostic studies using AI models [76], Checklist for Artificial Intelligence in Medical Imaging (CLAIM); and a checklist for AI in medical imaging [77].”
BackgroundMethodAI-driven deep CNN approach for multi-label pathology classification using chest X-Rays
Albahli, S., Rauf, H. T., Algosaibi, A. A., & Bălaş, V. E. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.495 · Cited by 95 · see citing works
“Even the patients could use this tool ( Mongan, Moy & Kahn, 2020 ; Ali et al., 2020 ; Tabares-Soto et al., 2020 ).”
MethodDeep learning: A primer for dentists and dental researchers
Mohammad‐Rahimi, H., Rokhshad, R., Bencharit, S., Krois, J., & Schwendicke, F. (2023) · Journal of Dentistry · doi:10.1016/j.jdent.2023.104430 · Cited by 97 · see citing works
Jacob, C., Brasier, N., Laurenzi, E., Heuss, S., Mougiakakou, S. G., Çöltekin, A., & Peter, M. K. (2024) · Journal of Medical Internet Research · doi:10.2196/67485 · Cited by 113 · see citing works
“…[14], Standards for Reporting of Diagnostic Accuracy Studies–Artificial Intelligence [22], Checklist for Artificial Intelligence in Medical Imaging [23], Prediction Model Risk of Bias Assessment Tool–Artificial Intelligence [24], and others, a unified international consensus on the evaluation of…”
Requirements and reliability of AI in the medical context
Balagurunathan, Y., Mitchell, R., & Naqa, I. E. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.02.024 · Cited by 91 · see citing works
“More specific recommendations for radiological application have been proposed, such as the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) [59].”
MethodAutonomous Chest Radiograph Reporting Using AI: Estimation of Clinical Impact
Plesner, L. L., Müller, F. C., Nybing, J. D., Laustrup, L. C., Rasmussen, F., Nielsen, O. W., Boesen, M., & Andersen, M. B. (2023) · Radiology · doi:10.1148/radiol.222268 · Cited by 89 · see citing works
“In recent years, there has been massive attention toward image classification with deep learning–based artificial intelligence (AI) models for radiology (2–4).”
Detecting white spot lesions on dental photography using deep learning: A pilot study
Askar, H., Krois, J., Rohrer, C., Mertens, S., Elhennawy, K., Ottolenghi, L., Mazur, M., Paris, S., & Schwendicke, F. (2021) · Journal of Dentistry · doi:10.1016/j.jdent.2021.103615 · Cited by 88 · see citing works
Orlhac, F., Nioche, C., Klyuzhin, I. S., Rahmim, A., & Buvat, I. (2021) · PET Clinics · doi:10.1016/j.cpet.2021.06.007 · Cited by 88 · see citing works
Reproducibility in machine‐learning‐based research: Overview, barriers, and drivers
Semmelrock, H., Ross‐Hellauer, T., Kopeinik, S., Theiler, D., Haberl, A., Thalmann, S., & Kowald, D. (2025) · AI Magazine · doi:10.1002/aaai.70002 · Cited by 118 · see citing works
Reporting Use of AI in Research and Scholarly Publication—JAMA Network Guidance
Flanagin, A., Pirracchio, R., Khera, R., Berkwits, M., Hswen, Y., & Bibbins-Domingo, K. B. (2024) · JAMA · doi:10.1001/jama.2024.3471 · Cited by 96 · see citing works
Enhancing trust in AI through industry self-governance.
Roski, J., Maier, E. J., Vigilante, K., Kane, E. A., & Matheny, M. E. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab065 · Cited by 83 · see citing works
Soffer, S., Morgenthau, A. S., Shimon, O., Barash, Y., Konen, E., Glicksberg, B. S., & Klang, E. (2021) · Academic Radiology · doi:10.1016/j.acra.2021.05.014 · Cited by 84 · see citing works
Translation of AI into oncology clinical practice
Naqa, I. E., Karolak, A. M., Luo, Y., Folio, L. R., Tarhini, A. A., Rollison, D. E. M., & Parodi, K. (2023) · Oncogene · doi:10.1038/s41388-023-02826-z · Cited by 83 · see citing works
“● Lack of adherence to the checklists mentioned above (TRIPOD-AI, CLAIM, MI-CLAIM, CLAMP, etc.) or more accurately, there is also lack of consensus standardizations among these standards, which creates its own confusion.”
BackgroundInfluentialChaudhari, A. S., Sandino, C. M., Cole, E. K., Larson, D. B., Gold, G. E., Vasanawala, S. S., Lungren, M. P., Hargreaves, B. A., & Langlotz, C. P. (2020) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27331 · Cited by 79 · see citing works
Artificial Intelligence in Periodontology: A Scoping Review
Scott, J., Biancardi, A. M., Jones, O. P. H., & Andrew, D. (2023) · Dentistry Journal · doi:10.3390/dj11020043 · Cited by 82 · see citing works
“Whilst some efforts have been made to standardise methodologies [83,84], these are still yet to be adopted or referenced in wider practice.”
BackgroundDesai, A., Calivá, F., Iriondo, C., Mortazi, A., Jambawalikar, S., Bağcı, U., Perslev, M., Igel, C., Dam, E. B., Gaj, S., et al. (2021) · Radiology Artificial Intelligence · doi:10.1148/ryai.2021200078 · Cited by 19 · see citing works
Zhong, J., Hu, Y., Si, L., Jia, G., Xing, Y., Zhang, H., & Yao, W. (2020) · European Radiology · doi:10.1007/s00330-020-07221-w · Cited by 77 · see citing works
“Other guidelines concerning artificial intelligence method also provide valuable references for RQS improvement [50, 51].”
BackgroundDetection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis
Din, M., Agarwal, S., Grzeda, M. T., Wood, D. A., Marc Modat, A., & Booth, T. C. (2022) · Journal of NeuroInterventional Surgery · doi:10.1136/jnis-2022-019456 · Cited by 77 · see citing works
- Authors
- Mongan, J., Moy, L., & Kahn, C. E.
- Affiliations
- University of California, San Francisco (US)
- Corresponding author
- Kahn, Charles E.
- Venue
- Radiology: Artificial Intelligence
- Published
- 2020-03-01
- Volume
- 2
- Issue
- 2
- Pages
- e200029
- ISSN
- 2638-6100 (ISSN-L)
- PMC
- PMC8017414
- OpenAlex
- W3013294478
- Semantic Scholar
- 5b34da942633f3a3afe01386341b8839a985ca0d
- Cited by
- 1,443
- References
- 31
- Citations by year
- 2026: 381 · 2025: 233 · 2024: 287 · 2023: 220 · 2022: 170 · 2021: 120 · 2020: 29
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Radiology Artificial Intelligence (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Boettcher Foundation
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Academic Writing and Publishing
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- artificial intelligence in medical imaging, reporting guidelines, author guidelines, peer review, model validation, model generalization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
